In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load COVID-19 dataset from Johns Hopkins University
url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
covid_data = pd.read_csv(url)
In [2]:
covid_data.head()
Out[2]:
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23
0 NaN Afghanistan 33.93911 67.709953 0 0 0 0 0 0 ... 209322 209340 209358 209362 209369 209390 209406 209436 209451 209451
1 NaN Albania 41.15330 20.168300 0 0 0 0 0 0 ... 334391 334408 334408 334427 334427 334427 334427 334427 334443 334457
2 NaN Algeria 28.03390 1.659600 0 0 0 0 0 0 ... 271441 271448 271463 271469 271469 271477 271477 271490 271494 271496
3 NaN Andorra 42.50630 1.521800 0 0 0 0 0 0 ... 47866 47875 47875 47875 47875 47875 47875 47875 47890 47890
4 NaN Angola -11.20270 17.873900 0 0 0 0 0 0 ... 105255 105277 105277 105277 105277 105277 105277 105277 105288 105288

5 rows × 1147 columns

In [3]:
covid_data.shape
Out[3]:
(289, 1147)
In [4]:
print(covid_data.isnull().sum())

Province/State    198
Country/Region      0
Lat                 2
Long                2
1/22/20             0
                 ... 
3/5/23              0
3/6/23              0
3/7/23              0
3/8/23              0
3/9/23              0
Length: 1147, dtype: int64
In [5]:
covid_data.drop(columns={'Province/State'},inplace=True)
In [6]:
melted_df = pd.melt(covid_data, id_vars=['Country/Region', 'Lat', 'Long'], var_name='Date', value_name='Cases')
In [7]:
melted_df
Out[7]:
Country/Region Lat Long Date Cases
0 Afghanistan 33.939110 67.709953 1/22/20 0
1 Albania 41.153300 20.168300 1/22/20 0
2 Algeria 28.033900 1.659600 1/22/20 0
3 Andorra 42.506300 1.521800 1/22/20 0
4 Angola -11.202700 17.873900 1/22/20 0
... ... ... ... ... ...
330322 West Bank and Gaza 31.952200 35.233200 3/9/23 703228
330323 Winter Olympics 2022 39.904200 116.407400 3/9/23 535
330324 Yemen 15.552727 48.516388 3/9/23 11945
330325 Zambia -13.133897 27.849332 3/9/23 343135
330326 Zimbabwe -19.015438 29.154857 3/9/23 264276

330327 rows × 5 columns

In [8]:
melted_df['Date'] = pd.to_datetime(melted_df['Date'])
In [9]:
# Calculate total cases per country
country_cases = covid_data.iloc[:, 3:].sum(axis=1)

# Add country cases to DataFrame
covid_data['Total Cases'] = country_cases

# Sort and plot top 10 countries
top_10 = covid_data.nlargest(10, 'Total Cases')
plt.figure(figsize=(10,6))
sns.barplot(x='Country/Region', y='Total Cases', data=top_10)
plt.title('Top 10 Countries by Total COVID-19 Cases')
plt.xlabel('Country')
plt.ylabel('Cases')
plt.xticks(rotation=90)
plt.show()
In [10]:
# Plot geographical distribution of COVID-19 cases
plt.figure(figsize=(12,6))
sns.scatterplot(x='Long', y='Lat', size='Total Cases', data=covid_data)
plt.title('Geographical Distribution of COVID-19 Cases')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()
In [11]:
top_10 = covid_data.nlargest(10, 'Total Cases')
In [12]:
top_10_countries = top_10['Country/Region'].tolist()
In [13]:
top_10_trend = melted_df[melted_df['Country/Region'].isin(top_10_countries)]
In [14]:
plt.figure(figsize=(14, 8))
sns.lineplot(data=top_10_trend, x='Date', y='Cases', hue='Country/Region', marker='')
plt.title('COVID-19 Cases Over Time for Top 10 Affected Countries')
plt.xlabel('Date')
plt.ylabel('Number of Cases')
plt.tight_layout()
plt.show()
In [15]:
top_5 = covid_data.nlargest(5, 'Total Cases')
In [16]:
top_5_countries = top_5['Country/Region'].tolist()
In [17]:
top_5_trend = melted_df[melted_df['Country/Region'].isin(top_5_countries)]
In [18]:
plt.figure(figsize=(12, 6))
sns.lineplot(data=top_5_trend, x='Date', y='Cases', hue='Country/Region')
plt.title('COVID-19 Cases Over Time for Top 5 Affected Countries')
plt.xlabel('Date')
plt.ylabel('Number of Cases')
plt.tight_layout()
plt.show()
In [19]:
ind_time_series = melted_df[melted_df['Country/Region']=='India']
plt.figure(figsize=(12,6))
plt.fill_between('Date','Cases',data=ind_time_series,color="palegreen", alpha=0.4)
plt.plot('Date','Cases',data=ind_time_series, color="green", alpha=0.6)
plt.title('COVID-19 Cases Over Time for India')
plt.xlabel('Date')
plt.ylabel('Number of Cases')
plt.show()
In [20]:
df = melted_df
In [21]:
from scipy.signal import find_peaks

df = df.sort_values(['Country/Region', 'Date'])
df['Daily_New_Cases'] = df.groupby('Country/Region')['Cases'].diff().fillna(0)
country_name = "US"
country_data = df[df['Country/Region'] == country_name].copy()

peaks, _ = find_peaks(country_data['Daily_New_Cases'], distance=30, height=500) 

plt.figure(figsize=(12, 6))

plt.plot(country_data['Date'], country_data['Daily_New_Cases'], label=f'{country_name} Daily New Cases', color='blue', alpha=0.7)

plt.plot(country_data['Date'].iloc[peaks], country_data['Daily_New_Cases'].iloc[peaks], "r^", label="Peaks (Waves)")

# Shading to indicate waves (optional)
for i in range(1, len(peaks)):
    plt.axvspan(country_data['Date'].iloc[peaks[i-1]], country_data['Date'].iloc[peaks[i]], color='orange', alpha=0.1)
    
# Customize the plot
plt.title(f'COVID-19 Waves Over Time for {country_name}')
plt.xlabel('Date')
plt.ylabel('Daily New Cases')
plt.legend()
plt.show()
In [22]:
df = df.sort_values(['Country/Region', 'Date'])
df['Daily_New_Cases'] = df.groupby('Country/Region')['Cases'].diff().fillna(0)
country_name = "India"
country_data = df[df['Country/Region'] == country_name].copy()

peaks, _ = find_peaks(country_data['Daily_New_Cases'], distance=30, height=500) 

plt.figure(figsize=(12, 6))

plt.plot(country_data['Date'], country_data['Daily_New_Cases'], label=f'{country_name} Daily New Cases', color='blue', alpha=0.7)

plt.plot(country_data['Date'].iloc[peaks], country_data['Daily_New_Cases'].iloc[peaks], "r^", label="Peaks (Waves)")

# Shading to indicate waves (optional)
for i in range(1, len(peaks)):
    plt.axvspan(country_data['Date'].iloc[peaks[i-1]], country_data['Date'].iloc[peaks[i]], color='green', alpha=0.1)
    
# Customize the plot
plt.title(f'COVID-19 Waves Over Time for {country_name}')
plt.xlabel('Date')
plt.ylabel('Daily New Cases')
plt.legend()
plt.show()
In [23]:
import pandas as pd
import plotly.express as px

# Aggregate total cases by country
total_cases_by_country = df.groupby('Country/Region')['Cases'].sum().reset_index()
total_cases_by_country.columns = ['Country', 'Total_Cases']

# Plot using Plotly's built-in GeoJSON map for countries
fig = px.choropleth(total_cases_by_country,
                    locations="Country",
                    locationmode="country names",
                    color="Total_Cases",
                    hover_name="Country",
                    color_continuous_scale="Reds",
                    title="Total COVID-19 Cases by Country")

fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(margin={"r":0,"t":50,"l":0,"b":0})
fig.show()
In [ ]: